ipw() is the generic for bring-your-own-model inverse probability weighted
estimation of causal effects. A method takes a fitted weighting or propensity
score model together with a fitted weighted outcome model and returns causal
effect estimates with standard errors that account for the two-step
estimation process.
Value
An object of class ipw, holding the causal effect estimates and
their standard errors alongside the models they came from. See new_ipw()
for the components and their order.
Details
This package defines the generic and the shared result class its methods
return. The methods themselves live in the packages that own the relevant
model classes, such as propensity for propensity score models and
balancing for balancing weight fits. Dispatch is on wt_mod, the weighting
object; each method documents the outcome model classes and further arguments
it accepts.
A method builds its return value with new_ipw() rather than a result object
of its own. The field names and their order are a cross-package contract, so
that an IPW estimate reads the same way whichever package produced it, and
constructing through new_ipw() is also what gives a method the shared
print() and as.data.frame() methods.
See also
new_ipw(), the constructor every method returns through, and the
propensity and balancing packages for methods.